1. Leveraging scriptable infrastructures, Towards a paradigm shift in software for data science Cloud Era Ltd 14 June 2013 karim.chine@cloudera.co.uk Karim Chine

2. 22 Outline Introduction Fragmentation and friction in the Data Science arena The understated potential of Cloud scriptability Elastic-R: Towards a universal platform for Data Science Elastic-R: Design and Technologies Overview Elastic-R: The scriptability toolset Demo Conclusion

3. 33 Introduction “The next information revolution is well underway. But it is not happening where information scientists, information executives, and the information industry in general are looking for it. It is not a revolution in technology, machinery, techniques, software, or speed. It is a revolution in concepts.” Peter Drucker. Forbes ASAP, August 24, 1998

4. 44 Introduction Science and the 4th Paradigm The e-Research Dancing Bears Experimental Science Theoretical Science Computational Science e-Science / Data-intensive Science The townspeople gather to see the wondrous sight as the massive, lumbering beast shambles and shuffles from paw to paw. The bear is really a terrible dancer, and the wonder isn't that the bear dances well but that the bear dances at all.

5. 5 Fragmentation and friction in the Data Science Arena www.scilab.org http://root.cern.ch www.sagemath.org www.sas.com office.microsoft.com www.mathworks.com www.scipy.org www.spss.com www.wolfram.com www.python.org  Open-source (GPL) software environment for statistical computing and graphics  Lingua franca of data analysis.  Repositories of contributed R packages related to a variety of problem domains in life sciences, social sciences, finance, econometrics, chemo metrics, etc. are growing at an exponential rate.  R is Super Glue

6. 6 The understated potential of Cloud scriptability 3D printers are becoming a common place:  Creating three-dimensional solid object of virtually any shape from a digital model.  Scripting the physical world  Sharing physical reality on Facebook is now as easy as sharing your holiday pictures

7. 7 Elastic-R: Towards a universal platform for Data Science Computational Components R packages, Wrapped C,C++,Fortran code, Python modules, Matlab Toolkits… Open source or commercial Computational Resources Clusters, grids, private or public clouds Free or pay-per-use Computational GUIs HTML5 and Desktop Workbench Built-in views /Plugins /Collaborative views Open source or commercial Computational Scripts R / Python / Matlab / Groovy Computational APIs Java / SOAP / REST, Stateless and stateful Computational Storage Local, NFS, FTP, Amazon S3, EBS Generated Computational Web Services Stateful or stateless, mapping of R objects/functions Elastic -R

8. 8 Elastic-R: Towards a universal platform for Data Science

9. 9 Public Clouds Private Cloud

10. 10 Elastic-R: Design and Technologies Overview You are here Your data is here

11. 11 Elastic-R: Design and Technologies Overview Remote Java/R Processes  Events-driven Remote Objects/Engines  R, Python, Mathematica, Matlab, Scilab,...  Collaborative Spreadsheets  Collaborative Scientific Graphics Canvas  Collaborative Dashboard with collaborative widgets

12. 12 Elastic-R: Design and Technologies Overview Elastic-R AMI 1 R 2.10 BioC 2.5 Elastic-R AMI 2 R 2.9 BioC 2.3 Elastic-R AMI 3 R 2.8 BioC 2.0 Elastic-R Amazon Machine Images Elastic-R EBS 1 Data Set XXX Elastic-R EBS 2 Data Set YYY Elastic-R EBS 3 Data Set ZZZ Elastic-R EBS 4 Data Set VVV Elastic-R AMI 2 R 2.9 BioC 2.3 Elastic-R EBS 4 Data Set VVV Amazon Elastic Block Stores Eastic-R AMI 2 R 2.9 BioC 2.3 Elastic-R.org Elastic-R EBS 4 Data Set VVV

13. 13 Modes of access Individuals with AWS accounts  Standard AMIs (Amazon Machine Images): paid-per-use to Amazon. For Academic use and trial purposes  Paid AMI: Paid-per-use software model. For business users Individuals without AWS accounts  Trial tokens, purchased tokens, tokens granted by other users.  Resources (data science engines) shared by other users  Individual subscribtions Companies/Educational & Research Institutions  Dedicated Platform and AMIs on an Amazon VPC (Virtual Private Cloud). Paid via subscribtion

14. 14 Elastic-R: The scriptability toolset Command Line Web Console SDK API

15. 15 Elastic-R: The scriptability toolset Command Line Web Console SDK API

16. 16 Elastic-R: The scriptability toolset WS generator rws.war + mapping.jar + pooling framework + R Java Bridge + JAX-WS - Servlets - Generated artifacts Eclipse Web Service Client Generator public static void main(String[] args) throws Exception { RGlobalEnvFunctionWeb g=new RGlobalEnvFunctionWebServiceLocator().getrGlobalEnvFunctionWebPort(); RNumeric x=new RNumeric(); x.setValue(new Double[]{6.0}); System.out.println(g.square(x).getValue()[0]); } square  function(x) {return(x^2) } typeInfo(square)  SimultaneousTypeSpecification( TypedSignature(x = "numeric"), returnType = "numeric")‏ Script / globals.r Script / rjmap.xml Deploy

17. 17 Elastic-R: The scriptability toolset API: Soap and Restful Web Services  Data Analysis Engines control  Data Analysis Engines Management (Life cycle, etc.)  Virtual appliances/artifacts management  Platform administartion SDKs: Java, R, Microsoft Office (Vba) Command line: elasticR package Html5 Workbench  Elastic-R artifacts management interface  Engines control interface

18. 18 Demo Register to Elastic-R academic and trial portal (www.elastic-r.org)www.elastic-r.org Create Data Science Engines using trial tokens Work with R, Python and Scientific Spreadsheets in the browser Share Data Science Engine and Collaborate Connect to the remote Data Science Engine from within a local R session, push and pull data, execute commands and show impact on spreadsheets and dashboards

19. 19 Conclusion Elastic -R unlocks the potential of the cloud for Data Scientists and analytical applications developers and improves dramatically their productivity The Data Science IT Factory chain, from resources acquisition to services and applications design and publication becomes:  Fully scriptable in R  Under the direct control of the Data Scientist and the developer  Fully traceable and reproducible Elastic-R can seamlessly extend any existing application or service and can be used as a collaboration backend Openness, security and reliability are among the key capabilities delivered

20. 20 What to do Next Register to Elastic-R and try the HTML 5 Workbench and the collaboration capabilities Download the R package elasticR and use it to access the cloud from local R sessions Download the Java SDK and create your first analytical application using AWS and advanced tools for programming with data. Get in touch with me to explore potential partnerships and collaborations

21. 21 Contact details Karim Chine karim.chine@cloudera.co.uk